It has become common for users of computers connected to the World Wide Web (the “web”) to employ web browsers and search engines to locate web pages (or “documents”) having specific content of interest to them (the users). A web-based commercial search engine may index tens of billions of web documents maintained by computers all over the world. Users of the computers compose queries, and the search engine identifies documents that match the queries to the extent that such documents include key words from the queries (known as the search results or result set).
Product classification in web-based “commerce search” involves associating categories to products offered by a large number of merchants. The categorized offers are used in many scenarios including product taxonomy browsing and matching merchant offers to products in a catalog-type view. These product offers typically comprise a short textual description of the product plus an image depicting that product. Traditional approaches to classifying such offers are focused on learning a good classifier based on the textual descriptions of the products, and deriving good classifiers—having a high degree of both precision and recall—for each available product is foundational to the provision of a high quality shopping experience.
However, classifiers derived exclusively from textual inputs can sometimes suffer from several shortcomings in the text upon which they rely—namely overlapping text, undescriptive text, and vocabulary usage discrepancies.